An information-theoretic method for multimodality medical image registration

In this paper, an information-theoretic approach for multimodal image registration is presented. In the proposed approach, image registration is carried out by maximizing a Tsallis entropy-based divergence using a modified simultaneous perturbation stochastic approximation algorithm. This divergence measure achieves its maximum value when the conditional intensity probabilities of the transformed target image given the reference image are degenerate distributions. Experimental results are provided to demonstrate the registration accuracy of the proposed approach in comparison to existing entropic image alignment techniques. The feasibility of the proposed algorithm is demonstrated on medical images from magnetic resonance imaging, computer tomography, and positron emission tomography.